intfloat/e5-mistral-7b-instruct
Primitive: /encode · Encode ·
Mistral
Improving Text Embeddings with Large Language Models. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
Dense
Overview
Hardware: — drives latency, throughput & cost
| Size | 7.1B params |
|---|---|
| Tasks | /encode |
| License | mit |
| Languages | en |
| Latency | 915 ms |
| Throughput | 3.0K tok/s |
| Cost | $0.074 /1M tok |
Cost is approximate — computed from list GPU prices; your actual price depends on the provider you deploy SIE with.
Embedding
| Output types | Dense |
|---|---|
| Dimensions | dense: 4,096 |
| Max sequence length | 4,096 |
| Inputs | text |
Benchmarks
NFCorpus
Biomedical literature search from NutritionFacts.org
Corpus: 3,593 Queries: 323
Quality
ndcg at 10 0.3932
map at 10 0.1477
mrr at 10 0.6024
Performance L4 b1 c16
Corpus 3.1K tok/s
Corpus p50 1.2s
Query 212 tok/s
Query p50 230.4ms
NanoFiQA2018Retrieval
Smaller subset of the FiQA financial QA dataset
Quality
ndcg at 10 0.5960
map at 10 0.5317
mrr at 10 0.6404
Performance L4 b1 c16
Corpus 2.9K tok/s
Corpus p50 670.1ms
Query 466 tok/s
Query p50 224.6ms
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